Guetzli: Perceptually Guided JPEG Encoder
arXiv:1703.04421
Abstract
Guetzli is a new JPEG encoder that aims to produce visually indistinguishable images at a lower bit-rate than other common JPEG encoders. It optimizes both the JPEG global quantization tables and the DCT coefficient values in each JPEG block using a closed-loop optimizer. Guetzli uses Butteraugli, our perceptual distance metric, as the source of feedback in its optimization process. We reach a 29-45% reduction in data size for a given perceptual distance, according to Butteraugli, in comparison to other compressors we tried. Guetzli's computation is currently extremely slow, which limits its applicability to compressing static content and serving as a proof- of-concept that we can achieve significant reductions in size by combining advanced psychovisual models with lossy compression techniques.
Cited by in corpus (4)
- Learning based Facial Image Compression with Semantic Fidelity Metric
- Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics
- JQF: Optimal JPEG Quantization Table Fusion by Simulated Annealing on Texture Images and Predicting Textures
- Binocular Tone Mapping with Improved Overall Contrast and Local Details